论文标题
机器学习的新颖实施，以从胸部CT对Covid-19进行有效的，可解释的诊断
A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT
论文作者
论文摘要
在与19 Covid-19一样紧急的全球健康危机中，已经迫切需要快速，可靠的诊断。当前，流行的测试方法（例如逆转录聚合酶链反应（RT-PCR））可能具有较高的假阴性率。因此，COVID-19患者未能准确地鉴定出来或足够快地治疗以防止病毒传播。但是，由于CT表现包含COVID-19的关键特征，因此医疗CT数据的最新兴起已经提出了有希望的途径。这项研究旨在从胸部CT扫描中基于机器学习的COVID-19检测中采取一种新颖的方法。首先，这项研究中使用的数据集源自三个主要来源，在923例患者病例中总共包括17,698个胸部CT切片。然后开发图像预处理算法，以通过排除无关的特征来减少噪声。还使用ExtricNETB7预培训模型实施了转移学习，以提供骨干体系结构并节省计算资源。最后，通过定位受感染区域并突出细化的像素细节，利用了几种解释性技术来定性地验证模型性能。提出的模型的总体准确度为0.927，灵敏度为0.958。解释性措施表明，该模型正确区分了与COVID-19胸部CT图像和正常对照有关的相关的关键特征。深度学习框架提供了有效的，人性化的Covid-19诊断，可以补充放射科医生的决策或作为替代筛查工具。未来的努力可能会洞悉感染严重程度，患者风险分层和预后。
In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Image preprocessing algorithms were then developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls. Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement radiologist decisions or serve as an alternative screening tool. Future endeavors may provide insight into infection severity, patient risk stratification, and prognosis.
